The invention is directed to a neural associative memory which, due to a high degree of parallel processing, has great advantages in specific, fast data-processing systems compared to conventional, comparable systems. Corresponding VLSI circuits having a low area and energy consumption are particularly required in the fast allocation of data as applied to sensor data, for example in image processing or in data processing in intelligent sensors.
Like conventional memories as well, associative memories are composed of a set of memory cells arranged in matrix-shaped fashion which, however, have a certain additional measure of functionality. In previous, trainable neural associative memories, the memory cells always represented a type of processor element or automaton that is respectively composed of a local memory and of a local executive sequencer.
The principle of what is referred to as the "associative matrix" is known from the article by Palm bearing the title, "On Associative Memory" in Biological Cybernetics 36, 1980, pages 19 through 31. What is involved there is a binary memory matrix in which the likewise binary input vectors X are read in, or respectively read out, row-by-row and the output vectors are read in, or respectively read out, column-by-column. The associative storing thus occurs on the basis of a simplified form of what is referred to as Hebb's training rule which, given application of a pattern pair X/Y to be associated to the matrix, locally decides in which way the status is to be modified at every matrix element m.sub.ij. Specifically, this seems as though the memory matrix is initially occupied entirely with "logical zeroes" in the initial condition. During the training process, m.sub.ij then applies for every memory cell, and its condition switches from "logical 0" to "logical 1" exactly when X.sub.i =Y.sub.j ="logical 1". When a "logical 1" was already written into the corresponding memory cell, then this status should be retained. Upon read-out of a stored data word, the corresponding input vector X is offered to the matrix and the output quantity Y' is formed in that the activities of the memory cells are added up column-by-column and a threshold decision is applied to this sum. A memory cell is thus considered active when x.sub.i =m.sub.ij ="1".
Principles, advantages and limits of analog calculating fields are known from the Proceedings of the IEEE Conference MicroNeuro '96, pages 68-79.